Measuring the gap between HMM-Based ASR and TTS

John Dines, Junichi Yamagishi, Simon King

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

The EMIME European project is conducting research in the development of technologies for mobile, personalized speech-to-speech translation systems. The hidden Markov model (HMM) is being used as the underlying technology in both automatic speech recognition (ASR) and text-to-speech synthesis (TTS) components; thus, the investigation of unified statistical modeling approaches has become an implicit goal of our research. As one of the first steps towards this goal, we have been investigating commonalities and differences between HMM-based ASR and TTS. In this paper, we present results and analysis of a series of experiments that have been conducted on English ASR and TTS systems measuring their performance with respect to phone set and lexicon, acoustic feature type and dimensionality, HMM topology, and speaker adaptation. Our results show that, although the fundamental statistical model may be essentially the same, optimal ASR and TTS performance often demands diametrically opposed system designs. This represents a major challenge to be addressed in the investigation of such unified modeling approaches.
Original languageEnglish
Pages (from-to)1046-1058
Number of pages13
JournalIEEE Journal of Selected Topics in Signal Processing
Issue number6
Early online date27 Sept 2010
Publication statusPublished - Dec 2010

Keywords / Materials (for Non-textual outputs)

  • speech synthesis
  • speech recognition
  • unified models


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